Master Thesis – Assessing Renewable Energy Potential under Disruptive Events - Köln

The energy transition in Germany relies on large-scale deployment of renewable energy. However, disruptive events caused by environmental or climate-related factors may significantly affect resource availability and infrastructure. This thesis will explore how disruptive scenarios impact renewable energy potentials across Germany by combining Python-based GIS workflows with energy system optimization. The aim is to quantify vulnerable regions and evaluate the resilience of the energy system under such conditions.

Your tasks:

• Processing regional-scale renewable energy potential datasets based on existing data
• Developing Python-based GIS workflows (GeoPandas, Shapely) to identify renewable sites / potentials within environmentally constrained areas and to quantify affected renewable potential areas under disruptive environmental events at regional scale
• Analyzing the implications of reduced renewable potentials on system design, storage, and resilience